Details

This NGC asset is a collection of diagnostic models built into Earth-2 MIP.\nDiagnostic models are a class of models that post-process the outputs of other weather/climate models into new quantities of interest.

Models

Precipitation AFNO

The FourCastNet diagnostic model which predicts total precipitation from 20 atmospheric variables.\nThe total precipitation, sourced from the ERA5 re-analysis dataset, represents the accumulated liquid and frozen water that falls to the Earth’s surface through rainfall and snow.\nIt is defined in units of length as the depth of water that would accumulate if spread evenly over a unit grid box of the model.\nThis model uses the Adaptive Fourier Neural Operator architecture, trained for 25 epochs.\nFor additional information on this model, Bill Collin's National Academies lecture provides an excellent overview of this model.

ClimateNet

This is a segmentation model trained on ClimateNet: a community-sourced human-expert-labeled curated dataset that captures tropical cyclones (TCs) and atmospheric rivers (ARs) in high-resolution climate model output.\nThe model provided is a convolutional neural network trained to predict both TCs and ARs from a small set of atmospheric variables.\nThe ClimateNet diagnostic model can be used to achieve various objectives such as: calculate a variety of TC and AR statistics at a fine-grained level; applied to different climate scenarios and different datasets; and rapidly analyzing large amounts of climate model output.

How to use

These diagnostic models are designed to be used using Earth-2 MIP package. Please refer to Earth-2 MIP documentation.

References

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Please refer to [Earth-2 MIP](https://github.com/NVIDIA/earth2mip) documentation.\n\n## References\n\n- [FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators](https://arxiv.org/abs/2202.11214)\n\n- [Adaptive Fourier Neural Operators: Efficient Token Mixers for Transformers](https://arxiv.org/abs/2111.13587)\n\n- [ClimateNet: an expert-labeled open dataset and deep learning architecture for enabling high-precision analyses of extreme weather](https://doi.org/10.5194/gmd-14-107-2021)\n\n- [The ERA5 global reanalysis](https://doi.org/10.1002/qj.3803)","displayName","Modulus Checkpoints: Earth-2 MIP Diagnostic Models",["D",1699381845583],"publisher","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/modulus/models/modulus_diagnostics","rawImageUrl","https://pbs.twimg.com/profile_images/1145524454170062848/U4lxVYEw_400x400.png","canGuestDownload","latestVersionSizeInBytes",275785568,"licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_114":1227,"_116":1228},"ngcApps",[1180],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1161":1162,"_1163":1164,"_1165":1166,"_1167":1168,"_1169":257,"_145":283,"_1170":1171,"_1172":57,"_1173":93,"_1174":93,"_1175":1176,"_1177":93,"_1178":1179},"amplitude",{"_1158":1159,"_794":-7,"_1160":32},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1152":1153,"_1154":1155,"_1156":1157},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1789130154780,"1.15.0","defaultSearchSuggestions",{"_1034":175,"_1035":1036,"_1037":1038,"_1039":1040},"announcements",[],"consentState",{"_320":1024,"_1025":93,"_1026":32,"_1027":32,"_1028":1029},"consentConfig",{"_302":303,"_304":305,"_306":307,"_308":309,"_310":311,"_312":313,"_314":315,"_316":317,"_318":319,"_320":321},"showContactAdmin",1789130154781,[],[],[297,298,299,300,301],{"_46":61,"_48":71,"_50":109,"_52":-5,"_54":-5,"_56":57},{"_46":61,"_48":69,"_50":109,"_52":-5,"_54":-5,"_56":57},{"_46":61,"_48":62,"_50":109,"_52":-5,"_54":-5,"_56":57},{"_46":58,"_48":59,"_50":109,"_52":-5,"_54":-5,"_56":57},{"_46":47,"_48":49,"_50":109,"_52":-5,"_54":-5,"_56":57},"mainInfoText","We and our third-party partners (including social media, advertising, and analytics partners) use cookies and other tracking technologies to collect, store, monitor, and process certain information about you when you visit our website. 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Name	Size	Updated	Actions
climatenet.zip	1.83 MB	February 28, 2025 UTC	curl wget Download
precipitation_afno.zip	261.1 MB	February 28, 2025 UTC	curl wget Download